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Display screen or portion thereof with graphical user interface

The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.

Cache in the air: exploiting content caching and delivery techniques for 5G systems

With the recent advent of 4G LTE networks, there has been increasing interest to better understand the performance and power characteristics, compared with 3G/WiFi networks. In this paper, we take one of the first steps in this direction.Using a publicly deployed tool we designed for Android called 4GTest attracting more than 3000 users within 2 months and extensive local experiments, we study the network performance of LTE networks and compare with other types of mobile networks. We observe LTE generally has significantly higher downlink and uplink throughput than 3G and even WiFi, with a median value of 13Mbps and 6Mbps, respectively. We develop the first empirically derived comprehensive power model of a commercial LTE network with less than 6% error rate and state transitions matching the specifications. Using a comprehensive data set consisting of 5-month traces of 20 smartphone users, we carefully investigate the energy usage in 3G, LTE, and WiFi networks and evaluate the impact of configuring LTE-related parameters. Despite several new power saving improvements, we find that LTE is as much as 23 times less power efficient compared with WiFi, and even less power efficient than 3G, based on the user traces and the long high power tail is found to be a key contributor. In addition, we perform case studies of several popular applications on Android in LTE and identify that the performance bottleneck for web-based applications lies less in the network, compared to our previous study in 3G [24]. Instead, the device's processing power, despite the significant improvement compared to our analysis two years ago, becomes more of a bottleneck.

/pdf/a-close-examination-of-performance-and-power-characteristics-2g7pjkqkb1.pdf

A close examination of performance and power characteristics of 4G LTE networks

Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities

The computation for today's intelligent personal assistants such as Apple Siri, Google Now, and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires significant amounts of data to be sent to the cloud over the wireless network and puts significant computational pressure on the datacenter. However, as the computational resources in mobile devices become more powerful and energy efficient, questions arise as to whether this cloud-only processing is desirable moving forward, and what are the implications of pushing some or all of this compute to the mobile devices on the edge.In this paper, we examine the status quo approach of cloud-only processing and investigate computation partitioning strategies that effectively leverage both the cycles in the cloud and on the mobile device to achieve low latency, low energy consumption, and high datacenter throughput for this class of intelligent applications. Our study uses 8 intelligent applications spanning computer vision, speech, and natural language domains, all employing state-of-the-art Deep Neural Networks (DNNs) as the core machine learning technique. We find that given the characteristics of DNN algorithms, a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.Using this insight, we design Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers. Neurosurgeon does not require per-application profiling. It adapts to various DNN architectures, hardware platforms, wireless networks, and server load levels, intelligently partitioning computation for best latency or best mobile energy. We evaluate Neurosurgeon on a state-of-the-art mobile development platform and show that it improves end-to-end latency by 3.1X on average and up to 40.7X, reduces mobile energy consumption by 59.5% on average and up to 94.7%, and improves datacenter throughput by 1.5X on average and up to 6.7X.

https://dl.acm.org/doi/pdf/10.1145/3037697.3037698

Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.

/pdf/characterizing-radio-resource-allocation-for-3g-networks-1j0duy06y8.pdf

Characterizing radio resource allocation for 3G networks

Recent studies have shown that in the wired broadband world, caching of HTTP objects results in substantial savings in network resources. What about cellular networks? This article examines the characteristics of HTTP traffic generated by millions of wireless users across one of the world's largest 3G cellular networks and explores the potential of forward caching. You can easily use the simple cost model provided here to determine the cost-benefit trade-offs for your own cellular network settings. This is the first large-scale caching analysis for cellular networks.

/pdf/to-cache-or-not-to-cache-the-3g-case-22n532kolj.pdf

To Cache or Not to Cache: The 3G Case

A method includes receiving a request for an edge cache address, and comparing a requester address to an anycast group. The method can further include providing an anycast edge cache address when the requestor address is in the anycast group. Alternatively, the method can further include determining an optimal cache server, and providing a unicast address of the optimal cache server when the requester address is not in the anycast group.

Hybrid unicast/anycast content distribution network system

Cellular networks have witnessed tremendous traffic growth recently, fueled by smartphones, tablets and new high speed broadband cellular access technologies. A key application driving that growth is video streaming. Yet very little is known about the characteristics of this traffic class. In this paper, we examine video traffic generated by three million users across one of the world's largest 3G cellular networks. This first deep dive into cellular video streaming shows that HLS, an adaptive bitrate streaming protocol, accounts for one third of the streaming video traffic and that it is common to see changes in encoding bitrates within a session. We also observe that most of the content is streamed at less than 255 Kbps and that only 40% of the videos are fully downloaded. Another key finding is that there exists significant potential for caching to deliver this content.

Oliver Spatscheck

Papers

A close examination of performance and power characteristics of 4G LTE networks

Characterizing radio resource allocation for 3G networks

To Cache or Not to Cache: The 3G Case

Hybrid unicast/anycast content distribution network system

Over the top video: the gorilla in cellular networks